Refik Anadol: Unsupervised—Machine Hallucinations
What would a machine mind dream of after “seeing” the vast collection of The Museum of Modern Art? In other words, if the corpus of images of the MoMA collection had been accomplished by a single artist, what would their dreams look like?
STATEMENT OF ARTIST INTENT
Unsupervised is a meditation on technology, creativity, and modern art. Anadol trained a sophisticated machine-learning model to interpret the publicly available data of MoMA’s collection. As the model “walks” through its conception of this vast range of works, it reimagines the history of modern art and dreams about what might have been—and what might be to come. In turn, Anadol incorporates site-specific input from the environment of the Museum’s Gund Lobby—changes in light, movement, acoustics, and the weather outside—to affect the continuously shifting imagery and sound.
ALTERNATE EXPRESSIONS WITH SOFT TECHNOLOGIES
Fluid dynamics has always been a massive source of inspiration for Anadol’s “Machine Hallucinations” project. The artist’s exploration of digital pigmentation and light through fluid solver algorithms could also inform the kinetic effects of soft sculptures that mimic fluid movement. Furthermore, his exploration of algorithms to simulate the movement of fluid could inspire the work of other AI-based artists.
APPLICATIONS TO THE ART CONTEXT: in what way will the technological expression be artistically meaningful?
The artist’s vision is to create a piece of work that can “handle data within a universe that it creates for itself” and strives to do this with his approach to data visualization’s latent space “as a locus for never-ending, self-generating contemplation”. The way Anadol brings together rich threads of information to weave beautiful visuals that incite feelings from viewers shows how data and generative algorithms can be used to create artistically meaningful work.
TECHNICAL PAPER WHICH ADDRESSES RELEVANT TECHNOLOGIES
Fluid Mechanics of Pneumatic Soft Robots
- this research investigates the movement of gas within an elastic field and explores how it can be applied to various realistic actuator configuration
- it provides a foundation for modeling fluid dynamics within fluid-actuated soft robots